UDL Lesson Plan Lesson Title: Chi_Square Test Grade Level: 12 Subject: Goodness of Fit Developed By: Cheryl Smith Unit: Inferential Statistics Abstract: In a goodness of fit test, the objective is to determine if a population has changed. Students will be able to determine this with an engaging activity of counting different colors of M&M candies in the individual packs they were given. Length of lesson: 45 minutes Pre Planning Big Idea (s) M&M Manufacturing Company in Trenton NJ insists that different colors of their chocolate candy are dispersed into a 1.69 oz bags by weight, not by count. The big idea is to find out if the M&M candy population has changed in individual bags. If it has, then we will have enough evidence against the manufacturer’s claim, otherwise we will accept the original claim made by the manufacturer. Essential Questions: How many red M&M’s have you counted? Is your observed count similar to the expected count? Repeat the Question above for all colors listed in your table. New Jersey Content Expectations: MA.9-12. - [Cluster Statement] - Make inferences and justify conclusions from sample surveys, experiments, and observational studies Objectives: 1) The objective of Goodness of Fit Test is to determine if a population has changed. 2)Students will be able to determine if the data give good reason to reject the hypothesis that the distribution of a categorical variable is the same in several populations. Brief Description of Summative Assessment: Students will record their findings in the tables that are provided to them. Students will be given an option to perform the test either using a graphing calculator or creating an excel spreadsheet for evidence of learning. Brief Description of the Opening Activity: In order to introduce students to the last of inferential testing technique called “Chi_Square”, I planned an opening activity hoping to engage everybody. I distributed 1.69 oz individually packed chocolate M&M Candy. Students will open their packs, transfer the candy in Ziploc bags to count the individual colors and record the numbers. Exploration: Students will take their observed counts of different colors of candy and multiply the numbers by the claimed percentages by the manufacturer. These numbers will be recorded as expected counts. Then, they will take the squared difference of the two counts, divide by two and record the numbers for each color. Once they are done, they will add the row of numbers and record these numbers as Chi_Square. Check for Understanding In this activity, what do you think we are trying to prove or disprove? The answer will reflect the understanding in correctly setting up the hypothesis. Why do you think we calculated the squared differences of observed and expected counts? Does this remind you of the any other inferential tests we did previously? In particular, what would you name this quantity. Explanation: I will try to utilize UDL in representing the lesson in several different ways including engaging opening activity. Assessment will also include different techniques of expression. Students will be able to use the table provided, as well as graphing calculator, or excel spreadsheet. UDL Lesson Plan Lesson Title: Chi_Square Test Grade Level: 12 Subject: Goodness of Fit Developed By: Cheryl Smith Unit: Inferential Statistics Abstract: In a goodness of fit test, the objective is to determine if a population has changed. Students will be able to determine this with an engaging activity of counting different colors of M&M candies in the individual packs they were given. Length of lesson: 45 minutes Pre Planning Big Idea (s) M&M Manufacturing Company in Trenton NJ insists that different colors of their chocolate candy are dispersed into a 1.69 oz bags by weight, not by count. The big idea is to find out if the M&M candy population has changed in individual bags. If it has, then we will have enough evidence against the manufacturer’s claim, otherwise we will accept the original claim made by the manufacturer. Essential Questions: How many red M&M’s have you counted? Is your observed count similar to the expected count? Repeat the Question above for all colors listed in your table. New Jersey Content Expectations: MA.9-12. - [Cluster Statement] - Make inferences and justify conclusions from sample surveys, experiments, and observational studies Objectives: 1) The objective of Goodness of Fit Test is to determine if a population has changed. 2)Students will be able to determine if the data give good reason to reject the hypothesis that the distribution of a categorical variable is the same in several populations. Brief Description of Summative Assessment: Students will record their findings in the tables that are provided to them. Students will be given an option to perform the test either using a graphing calculator or creating an excel spreadsheet for evidence of learning. Brief Description of the Opening Activity: In order to introduce students to the last of inferential testing technique called “Chi_Square”, I planned an opening activity hoping to engage everybody. I distributed 1.69 oz individually packed chocolate M&M Candy. Students will open their packs, transfer the candy in Ziploc bags to count the individual colors and record the numbers. Exploration: Students will take their observed counts of different colors of candy and multiply the numbers by the claimed percentages by the manufacturer. These numbers will be recorded as expected counts. Then, they will take the squared difference of the two counts, divide by two and record the numbers for each color. Once they are done, they will add the row of numbers and record these numbers as Chi_Square. Check for Understanding In this activity, what do you think we are trying to prove or disprove? The answer will reflect the understanding in correctly setting up the hypothesis. Why do you think we calculated the squared differences of observed and expected counts? Does this remind you of the any other inferential tests we did previously? In particular, what would you name this quantity. Explanation: I will try to utilize UDL in representing the lesson in several different ways including engaging opening activity. Assessment will also include different techniques of expression. Students will be able to use the table provided, as well as graphing calculator, or excel spreadsheet.
Lesson Title: Chi_Square Test
Grade Level: 12
Subject: Goodness of Fit
Developed By: Cheryl Smith
Unit: Inferential Statistics
Abstract:
In a goodness of fit test, the objective is to determine if a population has changed. Students will be able to determine this with an engaging activity of counting different colors of M&M candies in the individual packs they were given.
Length of lesson: 45 minutes
Pre Planning
Big Idea (s)
M&M Manufacturing Company in Trenton NJ insists that different colors of their chocolate candy are dispersed into a 1.69 oz bags by weight, not by count. The big idea is to find out if the M&M candy population has changed in individual bags. If it has, then we will have enough evidence against the manufacturer’s claim, otherwise we will accept the original claim made by the manufacturer.
Essential Questions:
How many red M&M’s have you counted? Is your observed count similar to the expected count?
Repeat the Question above for all colors listed in your table.
New Jersey Content Expectations:
MA.9-12. - [Cluster Statement] - Make inferences and justify conclusions from sample surveys, experiments, and observational studies
Objectives:
1) The objective of Goodness of Fit Test is to determine if a population has changed.
2) Students will be able to determine if the data give good reason to reject the hypothesis that the distribution of a categorical variable is the same in several populations.
Brief Description of Summative Assessment:
Students will record their findings in the tables that are provided to them.
Students will be given an option to perform the test either using a graphing calculator or creating an excel spreadsheet for evidence of learning.
Brief Description of the Opening Activity:
In order to introduce students to the last of inferential testing technique called “Chi_Square”, I planned an opening activity hoping to engage everybody. I distributed 1.69 oz individually packed chocolate M&M Candy. Students will open their packs, transfer the candy in Ziploc bags to count the individual colors and record the numbers.
Exploration:
Students will take their observed counts of different colors of candy and multiply the numbers by the claimed percentages by the manufacturer. These numbers will be recorded as expected counts. Then, they will take the squared difference of the two counts, divide by two and record the numbers for each color. Once they are done, they will add the row of numbers and record these numbers as Chi_Square.
Check for Understanding
In this activity, what do you think we are trying to prove or disprove? The answer will reflect the understanding in correctly setting up the hypothesis.
Why do you think we calculated the squared differences of observed and expected counts? Does this remind you of the any other inferential tests we did previously? In particular, what would you name this quantity.
Explanation:
I will try to utilize UDL in representing the lesson in several different ways including engaging opening activity. Assessment will also include different techniques of expression. Students will be able to use the table provided, as well as graphing calculator, or excel spreadsheet.
UDL Lesson Plan
Lesson Title: Chi_Square Test
Grade Level: 12
Subject: Goodness of Fit
Developed By: Cheryl Smith
Unit: Inferential Statistics
Abstract:
In a goodness of fit test, the objective is to determine if a population has changed. Students will be able to determine this with an engaging activity of counting different colors of M&M candies in the individual packs they were given.
Length of lesson: 45 minutes
Pre Planning
Big Idea (s)
M&M Manufacturing Company in Trenton NJ insists that different colors of their chocolate candy are dispersed into a 1.69 oz bags by weight, not by count. The big idea is to find out if the M&M candy population has changed in individual bags. If it has, then we will have enough evidence against the manufacturer’s claim, otherwise we will accept the original claim made by the manufacturer.
Essential Questions:
How many red M&M’s have you counted? Is your observed count similar to the expected count?
Repeat the Question above for all colors listed in your table.
New Jersey Content Expectations:
MA.9-12. - [Cluster Statement] - Make inferences and justify conclusions from sample surveys, experiments, and observational studies
Objectives:
1) The objective of Goodness of Fit Test is to determine if a population has changed.
2) Students will be able to determine if the data give good reason to reject the hypothesis that the distribution of a categorical variable is the same in several populations.
Brief Description of Summative Assessment:
Students will record their findings in the tables that are provided to them.
Students will be given an option to perform the test either using a graphing calculator or creating an excel spreadsheet for evidence of learning.
Brief Description of the Opening Activity:
In order to introduce students to the last of inferential testing technique called “Chi_Square”, I planned an opening activity hoping to engage everybody. I distributed 1.69 oz individually packed chocolate M&M Candy. Students will open their packs, transfer the candy in Ziploc bags to count the individual colors and record the numbers.
Exploration:
Students will take their observed counts of different colors of candy and multiply the numbers by the claimed percentages by the manufacturer. These numbers will be recorded as expected counts. Then, they will take the squared difference of the two counts, divide by two and record the numbers for each color. Once they are done, they will add the row of numbers and record these numbers as Chi_Square.
Check for Understanding
In this activity, what do you think we are trying to prove or disprove? The answer will reflect the understanding in correctly setting up the hypothesis.
Why do you think we calculated the squared differences of observed and expected counts? Does this remind you of the any other inferential tests we did previously? In particular, what would you name this quantity.
Explanation:
I will try to utilize UDL in representing the lesson in several different ways including engaging opening activity. Assessment will also include different techniques of expression. Students will be able to use the table provided, as well as graphing calculator, or excel spreadsheet.